Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis

Mwanga, E. P. et al. (2019) Detection of malaria parasites in dried human blood spots using mid-infrared spectroscopy and logistic regression analysis. Malaria Journal, 18, 341. (doi: 10.1186/s12936-019-2982-9) (PMID:31590669) (PMCID:PMC6781347)

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Background: Epidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots. Methods: Filter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm−1 to 500 cm−1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS. Results: Logistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen. Conclusion: These results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Ngowo, Halfan and Okumu, Professor Fredros and Baldini, Dr Francesco and Wynne, Professor Klaas and Ferguson, Professor Heather and Babayan, Dr Simon and Gonzalez Jimenez, Dr Mario and Maia, Dr Marta
Authors: Mwanga, E. P., Minja, E. G., Mrimi, E., González Jiménez, M., Swai, J. K., Abbasi, S., Ngowo, H. S., Siria, D. J., Mapua, S., Stica, C., Maia, M. F., Olotu, A., Sikulu-Lord, M. T., Baldini, F., Ferguson, H. M., Wynne, K., Selvaraj, P., Babayan, S. A., and Okumu, F. O.
College/School:College of Science and Engineering > School of Chemistry
College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
Journal Name:Malaria Journal
Publisher:BioMed Central
ISSN (Online):1475-2875
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Malaria Journal 18: 341
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
174132Development of a new tool for malaria mosquito surveillance to improve vector controlHeather FergusonMedical Research Council (MRC)MR/P025501/1Institute of Biodiversity, Animal Health and Comparative Medicine